CouchDB Performance Optimization and Tuning Quiz Quiz

Explore essential concepts of CouchDB performance tuning and optimization, including configuration, querying, compaction, and best practices for efficient data handling. Ideal for those looking to improve system speed, responsiveness, and reliability in distributed database environments.

1. Database Compaction

   Which operation is recommended to reduce storage footprint and improve read performance in CouchDB databases with frequent updates and deletions?

   1. Manual sharding
   2. Replica pruning
   3. Log rotation
   4. Database compaction

   Explanation: Database compaction removes outdated and deleted document revisions, helping reclaim disk space and optimize access speeds. Manual sharding does not directly impact database file size. Log rotation manages logs, not the primary database. Replica pruning is unrelated to storage optimization within a database instance.

2. View Indexing

   What action can help speed up view queries in CouchDB when the dataset is frequently updated?

   1. Increasing cache expiration
   2. Enabling cross-origin requests
   3. Reducing HTTP timeouts
   4. Triggering view index updates

   Explanation: Manually triggering view index updates ensures fresh and efficient view results after data changes. Increasing cache expiration does not update indexes. Reducing HTTP timeouts affects client connections, not view speed. Enabling cross-origin requests relates to security, not query performance.

3. Document Revisions

   Why should you minimize frequent document updates to the same record in CouchDB?

   1. It increases the number of stored revisions
   2. It bypasses validation rules
   3. It reduces storage security
   4. It deletes the document

   Explanation: Frequent updates lead to more revisions being stored, potentially increasing storage use and compaction needs. Updates do not delete documents. Validation rules remain enforced regardless of updates. Storage security is independent of revision count.

4. Network Latency

   How can network latency between nodes be reduced in a distributed CouchDB cluster?

   1. Placing nodes in the same data center
   2. Decreasing document size
   3. Enabling HTTP compression
   4. Using larger swap space

   Explanation: Locating nodes closer together physically reduces communication delays and improves cluster performance. HTTP compression helps reduce transfer size but not physical latency. Smaller documents may help bandwidth but not network delay. Swap space impacts memory, not network latency.

5. Bulk Operations

   Which method is most efficient for inserting multiple new documents into CouchDB?

   1. Syncing with external databases
   2. Editing the database file directly
   3. Using bulk document inserts
   4. Writing one document at a time

   Explanation: Bulk inserts allow many documents to be created or updated in a single request, reducing overhead. Writing documents one by one is slower due to repeated network and disk operations. Editing database files directly can corrupt data and is not supported. Syncing with external databases is for replication, not initial document creation.

6. Query Optimization

   What is a recommended practice for improving query speed when fetching documents matching specific fields in CouchDB?

   1. Increasing default timeouts
   2. Creating appropriate indexes
   3. Disabling continuous replication
   4. Enabling debug mode

   Explanation: Indexes allow couch queries to skip scanning all documents, resulting in faster retrieval times. Debug mode does not impact performance. Disabling continuous replication affects sync, not query speed. Increasing timeouts only changes wait periods, not actual performance.

7. Attachment Handling

   What is the recommended way to store and access large file attachments efficiently in CouchDB?

   1. Compress attachments manually before upload
   2. Disable attachments to improve speed
   3. Save attachments as large inline fields
   4. Store attachments externally and reference them

   Explanation: Large files should be stored outside the database with references to avoid performance issues and bloat. Inline storage makes documents bulky, slowing access. Manual compression helps reduce size but does not resolve database storage concerns. Disabling attachments altogether sacrifices needed functionality.

8. Resource Allocation

   Which server resource, if insufficient, is most likely to cause slow CouchDB performance during heavy concurrent access?

   1. Monitor resolution
   2. Number of active users
   3. Available RAM
   4. DNS record count

   Explanation: Limited RAM can lead to disk swapping and slowdowns under concurrent loads, impacting performance significantly. Monitor resolution has no effect on database speed. The number of DNS records is unrelated to resource usage. While user count matters, it's resource shortages like low RAM that cause the slowdowns.

9. Replication Settings

   How can adjusting replication settings in CouchDB help control the impact on network and system resources?

   1. By enabling admin-only access
   2. By setting replication batch size
   3. By turning off view indexing
   4. By changing attachment encoding

   Explanation: Replication batch size determines how many changes are sent per batch, influencing bandwidth and memory use. Disabling view indexing only affects query speed. Admin-only access is for security, not resource management. Attachment encoding impacts storage format, not replication load.

10. View Design

    Why is it important to design views with selective emit keys instead of emitting entire documents?

    1. To increase revision history length
    2. To ensure document deletion
    3. To bypass user authentication
    4. To minimize index file size and speed up queries

    Explanation: Emitting only relevant keys and values keeps view indexes small and responsive, boosting performance. Emitting documents does not affect user authentication, deletion, or revision history. The key focus is optimizing index size and lookup speed.